Decision comparison
Azure Synapse Analytics vs Amazon Redshift
Synapse and Redshift are the Azure and AWS answers to the same requirement, and the cloud an organisation already runs on usually decides between them. Synapse puts SQL warehousing, Spark and data integration in one workspace with Power BI and Entra alongside. Redshift is a mature warehouse with deep S3 integration through Spectrum, concurrency scaling for bursty load, and a serverless option that removes cluster sizing.
Direct comparison. These are reviewed substitutes bought for the same job, so the differences below are the ones that decide between them.
All 2 are cloud data warehouses.
Quick Comparison
| Decision factor | Azure Synapse Analytics | Amazon Redshift |
|---|---|---|
| What it is | Microsoft's unified analytics service: SQL warehousing, Spark and data integration in one workspace | AWS's managed data warehouse, with provisioned clusters and a serverless option |
| Cloud | Azure, integrated with Data Factory, Power BI, Azure ML and Entra identity | AWS, integrated with S3, Glue, QuickSight, SageMaker and IAM |
| Compute model | Dedicated SQL pools you provision and scale, a serverless SQL option, and Spark pools | Provisioned RA3 clusters with managed storage, or Redshift Serverless billed per capacity unit |
| Lake integration | Query files in Azure Data Lake Storage directly from serverless SQL | Redshift Spectrum queries data in S3 without loading it into the cluster |
| Built-in processing | Apache Spark pools share metadata and security with the SQL side | Spark runs in EMR or Glue rather than inside Redshift |
| Scaling behaviour | Scale a pool up or down, and pause it entirely when idle | Concurrency scaling adds capacity for bursts; serverless removes the sizing decision |
| Best fit | Organisations on Azure wanting SQL and Spark in one place | Organisations on AWS, particularly with large volumes already in S3 |
Azure Synapse Analytics
- What it is:
- Microsoft's unified analytics service: SQL warehousing, Spark and data integration in one workspace
- Cloud:
- Azure, integrated with Data Factory, Power BI, Azure ML and Entra identity
- Compute model:
- Dedicated SQL pools you provision and scale, a serverless SQL option, and Spark pools
- Lake integration:
- Query files in Azure Data Lake Storage directly from serverless SQL
- Built-in processing:
- Apache Spark pools share metadata and security with the SQL side
- Scaling behaviour:
- Scale a pool up or down, and pause it entirely when idle
- Best fit:
- Organisations on Azure wanting SQL and Spark in one place
Amazon Redshift
- What it is:
- AWS's managed data warehouse, with provisioned clusters and a serverless option
- Cloud:
- AWS, integrated with S3, Glue, QuickSight, SageMaker and IAM
- Compute model:
- Provisioned RA3 clusters with managed storage, or Redshift Serverless billed per capacity unit
- Lake integration:
- Redshift Spectrum queries data in S3 without loading it into the cluster
- Built-in processing:
- Spark runs in EMR or Glue rather than inside Redshift
- Scaling behaviour:
- Concurrency scaling adds capacity for bursts; serverless removes the sizing decision
- Best fit:
- Organisations on AWS, particularly with large volumes already in S3
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Azure Synapse Analytics | Amazon Redshift |
|---|---|---|
| Search interest(Market interest) | 0 | 1 |
| Hacker News mentions, 90d(Community interest) | 0 | 0 |
| npm weekly downloads(Developer adoption) | 446 | 229.8k |
| PyPI weekly downloads(Developer adoption) | 1.2M | 10.2M |
| Stack Overflow questions(Community interest) | 3.0k | 8.8k |
| GitHub commits, 90d(Developer adoption) | Not available | 9 |
| GitHub stars(Developer adoption) | Not available | 71 |
| Product Hunt comments(Community interest) | Not available | 1 |
| Product Hunt reviews(Community interest) | Not available | 0 |
| Product Hunt votes(Community interest) | Not available | 68 |
As of September 14, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Azure Synapse Analytics
September 14, 2026Package vulnerabilities
npm · @azure/synapse-artifacts@1.0.0-beta.16 · PyPI · azure-synapse-artifacts@0.22.0
0 vulnerabilities
across 2 packages
Repository security score
Not available
Amazon Redshift
September 14, 2026Package vulnerabilities
npm · @aws-sdk/client-redshift@3.1131.0 · PyPI · redshift-connector@2.1.16
0 vulnerabilities
across 2 packages
Repository security score
github.com/aws/amazon-redshift-jdbc-driver
4.5/10
Feature Comparison
| Feature | Azure Synapse Analytics | Amazon Redshift |
|---|---|---|
| Query | ||
| Standard SQL analytics | Full support | Full support |
| Serverless query option | Full support | Full support |
| Query data in object storage directly | Full support | Full support |
| Materialised views | Full support | Full support |
| Processing | ||
| Built-in Spark | Full support | Not verified |
| Data integration pipelines in the same product | Full support | Partial support |
| In-database machine learning | Partial support | Full support |
| Streaming ingestion | Partial support | Full support |
| Operations | ||
| Pause compute to stop charges | Full support | Full support |
| Automatic concurrency scaling | Partial support | Full support |
| Managed storage separate from compute | Full support | Full support |
| No cluster sizing required | Partial support | Partial support |
| Ecosystem | ||
| Native BI integration | Full support | Full support |
| Cloud-native identity and governance | Full support | Full support |
| Mature partner tooling | Full support | Full support |
| Multi-cloud portability | Partial support | Partial support |
Query
Standard SQL analytics
Serverless query option
Query data in object storage directly
Materialised views
Processing
Built-in Spark
Data integration pipelines in the same product
In-database machine learning
Streaming ingestion
Operations
Pause compute to stop charges
Automatic concurrency scaling
Managed storage separate from compute
No cluster sizing required
Ecosystem
Native BI integration
Cloud-native identity and governance
Mature partner tooling
Multi-cloud portability
Which to choose
Synapse and Redshift are the Azure and AWS answers to the same requirement, and the cloud an organisation already runs on usually decides between them. Synapse puts SQL warehousing, Spark and data integration in one workspace with Power BI and Entra alongside. Redshift is a mature warehouse with deep S3 integration through Spectrum, concurrency scaling for bursty load, and a serverless option that removes cluster sizing.
Best-fit scenarios
Choose Azure Synapse Analytics if:
Choose Azure Synapse when the organisation is on Azure and you want warehousing and Spark in one workspace. Shared metadata and security between SQL and Spark removes boundaries that are otherwise separate services to wire together, Data Factory handles pipelines in the same product, and Power BI and Entra integrate natively. Dedicated pools can be paused when idle to stop the charge.
Choose Amazon Redshift if:
Choose Amazon Redshift when you are on AWS, especially with large volumes already in S3. Spectrum queries that data in place without loading it, RA3 nodes separate managed storage from compute, and concurrency scaling absorbs bursts without permanent capacity. Redshift Serverless removes the cluster-sizing decision when you would rather not make it.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
How much does the existing cloud really matter?
More than the feature comparison suggests. Analytical workloads move large volumes, and cross-cloud egress is charged continuously without appearing on either pricing page. Identity and governance integrate natively within a cloud and require federation across them. And the BI tool, ML platform and pipeline service your organisation already licenses generally come from the same vendor. These are substantive reasons rather than inertia.
What does Spectrum change about Redshift?
It means the warehouse is not the only place data has to live. Cold or rarely queried data can stay in S3 and still be queried, joined against warehouse tables in the same SQL. That changes the economics of retention: you are not choosing between loading everything and losing access to it. Synapse's serverless SQL over Azure Data Lake Storage is the equivalent capability on the other side.
Is built-in Spark worth choosing a platform for?
It is worth something if you need Spark and want it beside SQL with shared metadata and access control. Synapse offers that in one workspace. On AWS, Spark lives in EMR or Glue — capable services, and separate ones to configure, secure and monitor. Whether a single workspace is a simplification or an unnecessary coupling depends on whether the same team does both jobs.
How do we control cost on either?
On provisioned capacity, by sizing correctly and pausing when idle — which sounds obvious and is the most commonly missed saving, because nobody remembers to pause. On serverless or per-scan pricing, by partitioning tables and writing queries that prune properly, since an unfiltered scan of a large table costs real money. Both platforms offer committed pricing that caps the bill and queues users instead.
Which handles concurrency better?
Redshift has concurrency scaling, which adds transient capacity when queues form and removes it afterwards, so bursty BI traffic does not require permanently provisioned headroom. Synapse scales pools up and down, which is more deliberate and less automatic. If your load is spiky — a dashboard refresh every morning, quiet afternoons — that difference shows up in both performance and bill.